Fast ensemble empirical mode decomposition model for forecasting crude oil and condensates
DOI:
https://doi.org/10.11113/mjfas.v13n3.531Keywords:
Crude oil and Condensates, Fast Ensemble Empirical Mode Decomposition (FEEMD), Tapis-blend Oil Prices, Forecasting,Abstract
Crude oil and condensates supply and demand strives to be main authority of the sustenance of almost all country’s economy. The sudden rise in the oil price has forced the government to forecast the supply and demand of crude oil and condensates in order to make sure that the amount of crude oil meets the supply and demand of the country. Accurate forecasts can save cost, foresee scarcity of demand, and help in budgeting profit. In addition, predicting crude oil and condensate data is frequently proven to be a demanding task considering the various intricacies of oil data pattern. The main objective of this study was to forecast crude oil and condensates demand data in Malaysia using Fast Ensemble Empirical Mode Decomposition (FEEMD) model. The forecasting process using FEEMD model was performed in order to achieve the most desirable forecast accuracy of the crude oil and condensates data. The FEEMD model is an extension of the Empirical Mode Decomposition (EMD) model whereby white noise signal was added to the existing signal in the sifting process. The effectiveness of the proposed forecasting method was compared to other traditional models of ARIMA, ARIMAX and GARCH. The results revealed that the proposed FEEMD method for forecasting crude oil and condensates data was very promising as it achieved good forecast accuracy.
References
Aizenberg, I., Sheremetov, L., Villa-Vargas, L., Martinez-Muñoz, J. 2016. Multilayer neural network with multi-valued neurons in time series forecasting of oil production. Neurocomputing. 175(b), 980-989.
Bollerslev, T. 1986. Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics. 31, 307–327.
Box, G. E. P., G. M. Jenkins, G. M., Reinsel, G. C. 2008. Time Series Analysis: Forecasting and Control, 4th ed. New Jersey: John Wiley and Sons, Inc.
Chakra, N. C., Song, K-Y., Gupta, M. M., Saraf, D. N. 2013. An innovative neural forecast of cumulative oil production from a petroleum reservoir employing higher-order neural networks (HONNs). Journal of Petroleum Science and Engineering. 106, 18-33.
Chuanping, Z., Ruirui, Z., Xueping, Y., Lin, Y. 2011. Analysis of china's oil self- sufficiency. 2011 International Conference on Management and Service Science. Wuhan, China. 12-14 August 2011. 1-5.
He, K., Yu, L., Lai, K. K. 2012. Crude oil price analysis and forecasting using wavelet decomposed ensemble model. Energy. 46(1), 564-574.
Huang, N. E., Shen, Z., Long, S. R., Long, S. R., Wu, M. C., Shih, H. H., Zheng, Q., Yen, N-C., Tung, C. C., Liu, H. H. 1998. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proceedings of the Royal Society. A Mathematical, Physical and Engineering Sciences. 454 (1971), 903-995.
Li, J. P., Tanga, L., Sun, X. L., Yu, L. A., Hee, W., Yang, Y. Y. 2012. Country risk forecasting for major oil exporting countries: A decomposition hybrid approach. Computers and Industrial Engineering. 63(3), 641-651.
Liu, H., Tian, H., Liang, X., Y. Li, 2015. New wind speed forecasting approaches using fast ensemble empirical mode decomposition, genetic algorithm, mind evolutionary algorithm and artificial neural networks. Renewable Energy. 83, 1066-1075.
Mohammadpoor, M., Torabi, F. 2015. Extensive experimental investigation of the effect of drainage height and solvent type on the stabilized drainage rate in vapour extraction (VAPEX) process. Petroleum. 1, 187-199.
Pankratz, A. 1991. Forecasting with Dynamic Regression Models. Hoboken, NJ, USA: John Wiley & Sons, Inc.
Seyedan, S. M., Adli, N. N., Ebadati, O. M. 2015. Performance analysis and forecasting on crude oil: novel support vector regression application to market demand. 2015 International Conference on Soft Computing Techniques and Implementations- (ICSCTI). 8-10 October. Faridabad, India: Advanced Computing, IEEE, 126-130.
Sheremetov, L. B., González-Sánchez, A., López-Yáñez, I., Ponomarev, A. V. 2013. Time series forecasting: applications to the upstream oil and gas supply chain. 7th IFAC Conference on Manufacturing Modelling, Management, and Control. International Federation of Automatic Control. 19-21 June. Saint Petersburg, Russia, 957-962.
Wu, Z. Huang, N. E. 2009. Ensemble empirical mode decomposition: a noise-assisted data analysis method. Advances in Adaptive Data Analysis. 1(1), 1-41.
Yu, L., Dai, W., and Tang, Y. 2016. A Novel decomposition ensemble model with extended extreme learning machine for crude oil price forecasting. Engineering Applications of Artificial Intelligence. 47, 110-121.
Yu, L., Wang, Z., Tang, L. 2015. A decomposition–ensemble model with data-characteristic-driven reconstruction for crude oil price forecasting. Applied Energy. 156, 251-267.
Yua, L., Wanga, S., Laib, K. K. 2008. Forecasting crude oil price with an emd-based neural network ensemble learning paradigm. Energy Economics. 30(5), 2623-2635.
Zhang,Q., Chen, N., Zhao, J. 2010. Combined forecast model of refined oil demand based on grey theory. 2010 International Symposium on Intelligence Information Processing and Trusted Computing. IEEE Computer Society, 145-148.
